In [1]:
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(style="whitegrid")
import matplotlib.pyplot as plt
from collections import Counter
%matplotlib inline
In [2]:
import warnings
warnings.filterwarnings('ignore')

Import Data Set¶

In [3]:
fifa=pd.read_csv(r'E:\DS Course\2. July\28th\Seaborn\FIFA.csv')
In [4]:
fifa
Out[4]:
Unnamed: 0 ID Name Age Photo Nationality Flag Overall Potential Club ... Composure Marking StandingTackle SlidingTackle GKDiving GKHandling GKKicking GKPositioning GKReflexes Release Clause
0 0 158023 L. Messi 31 https://cdn.sofifa.org/players/4/19/158023.png Argentina https://cdn.sofifa.org/flags/52.png 94 94 FC Barcelona ... 96.0 33.0 28.0 26.0 6.0 11.0 15.0 14.0 8.0 €226.5M
1 1 20801 Cristiano Ronaldo 33 https://cdn.sofifa.org/players/4/19/20801.png Portugal https://cdn.sofifa.org/flags/38.png 94 94 Juventus ... 95.0 28.0 31.0 23.0 7.0 11.0 15.0 14.0 11.0 €127.1M
2 2 190871 Neymar Jr 26 https://cdn.sofifa.org/players/4/19/190871.png Brazil https://cdn.sofifa.org/flags/54.png 92 93 Paris Saint-Germain ... 94.0 27.0 24.0 33.0 9.0 9.0 15.0 15.0 11.0 €228.1M
3 3 193080 De Gea 27 https://cdn.sofifa.org/players/4/19/193080.png Spain https://cdn.sofifa.org/flags/45.png 91 93 Manchester United ... 68.0 15.0 21.0 13.0 90.0 85.0 87.0 88.0 94.0 €138.6M
4 4 192985 K. De Bruyne 27 https://cdn.sofifa.org/players/4/19/192985.png Belgium https://cdn.sofifa.org/flags/7.png 91 92 Manchester City ... 88.0 68.0 58.0 51.0 15.0 13.0 5.0 10.0 13.0 €196.4M
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
18202 18202 238813 J. Lundstram 19 https://cdn.sofifa.org/players/4/19/238813.png England https://cdn.sofifa.org/flags/14.png 47 65 Crewe Alexandra ... 45.0 40.0 48.0 47.0 10.0 13.0 7.0 8.0 9.0 €143K
18203 18203 243165 N. Christoffersson 19 https://cdn.sofifa.org/players/4/19/243165.png Sweden https://cdn.sofifa.org/flags/46.png 47 63 Trelleborgs FF ... 42.0 22.0 15.0 19.0 10.0 9.0 9.0 5.0 12.0 €113K
18204 18204 241638 B. Worman 16 https://cdn.sofifa.org/players/4/19/241638.png England https://cdn.sofifa.org/flags/14.png 47 67 Cambridge United ... 41.0 32.0 13.0 11.0 6.0 5.0 10.0 6.0 13.0 €165K
18205 18205 246268 D. Walker-Rice 17 https://cdn.sofifa.org/players/4/19/246268.png England https://cdn.sofifa.org/flags/14.png 47 66 Tranmere Rovers ... 46.0 20.0 25.0 27.0 14.0 6.0 14.0 8.0 9.0 €143K
18206 18206 246269 G. Nugent 16 https://cdn.sofifa.org/players/4/19/246269.png England https://cdn.sofifa.org/flags/14.png 46 66 Tranmere Rovers ... 43.0 40.0 43.0 50.0 10.0 15.0 9.0 12.0 9.0 €165K

18207 rows × 89 columns

In [5]:
fifa.head()
Out[5]:
Unnamed: 0 ID Name Age Photo Nationality Flag Overall Potential Club ... Composure Marking StandingTackle SlidingTackle GKDiving GKHandling GKKicking GKPositioning GKReflexes Release Clause
0 0 158023 L. Messi 31 https://cdn.sofifa.org/players/4/19/158023.png Argentina https://cdn.sofifa.org/flags/52.png 94 94 FC Barcelona ... 96.0 33.0 28.0 26.0 6.0 11.0 15.0 14.0 8.0 €226.5M
1 1 20801 Cristiano Ronaldo 33 https://cdn.sofifa.org/players/4/19/20801.png Portugal https://cdn.sofifa.org/flags/38.png 94 94 Juventus ... 95.0 28.0 31.0 23.0 7.0 11.0 15.0 14.0 11.0 €127.1M
2 2 190871 Neymar Jr 26 https://cdn.sofifa.org/players/4/19/190871.png Brazil https://cdn.sofifa.org/flags/54.png 92 93 Paris Saint-Germain ... 94.0 27.0 24.0 33.0 9.0 9.0 15.0 15.0 11.0 €228.1M
3 3 193080 De Gea 27 https://cdn.sofifa.org/players/4/19/193080.png Spain https://cdn.sofifa.org/flags/45.png 91 93 Manchester United ... 68.0 15.0 21.0 13.0 90.0 85.0 87.0 88.0 94.0 €138.6M
4 4 192985 K. De Bruyne 27 https://cdn.sofifa.org/players/4/19/192985.png Belgium https://cdn.sofifa.org/flags/7.png 91 92 Manchester City ... 88.0 68.0 58.0 51.0 15.0 13.0 5.0 10.0 13.0 €196.4M

5 rows × 89 columns

In [6]:
fifa.tail()
Out[6]:
Unnamed: 0 ID Name Age Photo Nationality Flag Overall Potential Club ... Composure Marking StandingTackle SlidingTackle GKDiving GKHandling GKKicking GKPositioning GKReflexes Release Clause
18202 18202 238813 J. Lundstram 19 https://cdn.sofifa.org/players/4/19/238813.png England https://cdn.sofifa.org/flags/14.png 47 65 Crewe Alexandra ... 45.0 40.0 48.0 47.0 10.0 13.0 7.0 8.0 9.0 €143K
18203 18203 243165 N. Christoffersson 19 https://cdn.sofifa.org/players/4/19/243165.png Sweden https://cdn.sofifa.org/flags/46.png 47 63 Trelleborgs FF ... 42.0 22.0 15.0 19.0 10.0 9.0 9.0 5.0 12.0 €113K
18204 18204 241638 B. Worman 16 https://cdn.sofifa.org/players/4/19/241638.png England https://cdn.sofifa.org/flags/14.png 47 67 Cambridge United ... 41.0 32.0 13.0 11.0 6.0 5.0 10.0 6.0 13.0 €165K
18205 18205 246268 D. Walker-Rice 17 https://cdn.sofifa.org/players/4/19/246268.png England https://cdn.sofifa.org/flags/14.png 47 66 Tranmere Rovers ... 46.0 20.0 25.0 27.0 14.0 6.0 14.0 8.0 9.0 €143K
18206 18206 246269 G. Nugent 16 https://cdn.sofifa.org/players/4/19/246269.png England https://cdn.sofifa.org/flags/14.png 46 66 Tranmere Rovers ... 43.0 40.0 43.0 50.0 10.0 15.0 9.0 12.0 9.0 €165K

5 rows × 89 columns

In [7]:
fifa.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18207 entries, 0 to 18206
Data columns (total 89 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Unnamed: 0                18207 non-null  int64  
 1   ID                        18207 non-null  int64  
 2   Name                      18207 non-null  object 
 3   Age                       18207 non-null  int64  
 4   Photo                     18207 non-null  object 
 5   Nationality               18207 non-null  object 
 6   Flag                      18207 non-null  object 
 7   Overall                   18207 non-null  int64  
 8   Potential                 18207 non-null  int64  
 9   Club                      17966 non-null  object 
 10  Club Logo                 18207 non-null  object 
 11  Value                     18207 non-null  object 
 12  Wage                      18207 non-null  object 
 13  Special                   18207 non-null  int64  
 14  Preferred Foot            18159 non-null  object 
 15  International Reputation  18159 non-null  float64
 16  Weak Foot                 18159 non-null  float64
 17  Skill Moves               18159 non-null  float64
 18  Work Rate                 18159 non-null  object 
 19  Body Type                 18159 non-null  object 
 20  Real Face                 18159 non-null  object 
 21  Position                  18147 non-null  object 
 22  Jersey Number             18147 non-null  float64
 23  Joined                    16654 non-null  object 
 24  Loaned From               1264 non-null   object 
 25  Contract Valid Until      17918 non-null  object 
 26  Height                    18159 non-null  object 
 27  Weight                    18159 non-null  object 
 28  LS                        16122 non-null  object 
 29  ST                        16122 non-null  object 
 30  RS                        16122 non-null  object 
 31  LW                        16122 non-null  object 
 32  LF                        16122 non-null  object 
 33  CF                        16122 non-null  object 
 34  RF                        16122 non-null  object 
 35  RW                        16122 non-null  object 
 36  LAM                       16122 non-null  object 
 37  CAM                       16122 non-null  object 
 38  RAM                       16122 non-null  object 
 39  LM                        16122 non-null  object 
 40  LCM                       16122 non-null  object 
 41  CM                        16122 non-null  object 
 42  RCM                       16122 non-null  object 
 43  RM                        16122 non-null  object 
 44  LWB                       16122 non-null  object 
 45  LDM                       16122 non-null  object 
 46  CDM                       16122 non-null  object 
 47  RDM                       16122 non-null  object 
 48  RWB                       16122 non-null  object 
 49  LB                        16122 non-null  object 
 50  LCB                       16122 non-null  object 
 51  CB                        16122 non-null  object 
 52  RCB                       16122 non-null  object 
 53  RB                        16122 non-null  object 
 54  Crossing                  18159 non-null  float64
 55  Finishing                 18159 non-null  float64
 56  HeadingAccuracy           18159 non-null  float64
 57  ShortPassing              18159 non-null  float64
 58  Volleys                   18159 non-null  float64
 59  Dribbling                 18159 non-null  float64
 60  Curve                     18159 non-null  float64
 61  FKAccuracy                18159 non-null  float64
 62  LongPassing               18159 non-null  float64
 63  BallControl               18159 non-null  float64
 64  Acceleration              18159 non-null  float64
 65  SprintSpeed               18159 non-null  float64
 66  Agility                   18159 non-null  float64
 67  Reactions                 18159 non-null  float64
 68  Balance                   18159 non-null  float64
 69  ShotPower                 18159 non-null  float64
 70  Jumping                   18159 non-null  float64
 71  Stamina                   18159 non-null  float64
 72  Strength                  18159 non-null  float64
 73  LongShots                 18159 non-null  float64
 74  Aggression                18159 non-null  float64
 75  Interceptions             18159 non-null  float64
 76  Positioning               18159 non-null  float64
 77  Vision                    18159 non-null  float64
 78  Penalties                 18159 non-null  float64
 79  Composure                 18159 non-null  float64
 80  Marking                   18159 non-null  float64
 81  StandingTackle            18159 non-null  float64
 82  SlidingTackle             18159 non-null  float64
 83  GKDiving                  18159 non-null  float64
 84  GKHandling                18159 non-null  float64
 85  GKKicking                 18159 non-null  float64
 86  GKPositioning             18159 non-null  float64
 87  GKReflexes                18159 non-null  float64
 88  Release Clause            16643 non-null  object 
dtypes: float64(38), int64(6), object(45)
memory usage: 12.4+ MB
In [8]:
fifa['Body Type'].value_counts()
Out[8]:
Normal                 10595
Lean                    6417
Stocky                  1140
Messi                      1
C. Ronaldo                 1
Neymar                     1
Courtois                   1
PLAYER_BODY_TYPE_25        1
Shaqiri                    1
Akinfenwa                  1
Name: Body Type, dtype: int64

Visualize distribution of Age variable with Seaborn distplot() function

Seaborn distplot() function flexibly plots a univariate distribution of observations.

This function combines the matplotlib hist function (with automatic calculation of a good default bin size) with the seaborn kdeplot() and rugplot() functions.

So, let's visualize the distribution of Age variable with Seaborn distplot() function.

Explore Age variable¶

In [9]:
f,ax=plt.subplots(figsize=(8,6))
x=fifa['Age']
ax=sns.distplot(x,bins=10)
plt.show()

the Age variable is slightly positively skewed.

We can use Pandas series object to get an informative axis label as follows-

In [10]:
f,ax=plt.subplots(figsize=(8,6))
x=fifa['Age']
x=pd.Series(x,name="Age variable")
ax=sns.distplot(x,bins=10)
plt.show()
In [11]:
f,ax=plt.subplots(figsize=(8,6))
x=fifa['Age']
ax=sns.distplot(x,bins=10,vertical=True)
plt.show()
In [12]:
f,ax=plt.subplots(figsize=(8,6))
x=fifa['Age']
x=pd.Series(x,name="Age variable")
ax=sns.kdeplot(x)
plt.show()
In [13]:
f,ax=plt.subplots(figsize=(8,6))
x=fifa['Age']
x=pd.Series(x,name="Age Variable")
ax=sns.kdeplot(x,shade=True,color='r')
plt.show()
In [14]:
f,ax=plt.subplots(figsize=(8,6))
x=fifa['Age']
ax=sns.distplot(x,kde=False,rug=True,bins=10)
plt.show()
In [15]:
f,ax=plt.subplots(figsize=(8,6))
x=fifa['Age']
ax=sns.distplot(x,hist=False,rug=True,bins=10)
plt.show()

Explore Preferred Foot variable¶

In [16]:
fifa['Preferred Foot'].nunique()
Out[16]:
2
In [17]:
fifa['Preferred Foot'].unique()
Out[17]:
array(['Left', 'Right', nan], dtype=object)
In [18]:
fifa['Preferred Foot'].value_counts()
Out[18]:
Right    13948
Left      4211
Name: Preferred Foot, dtype: int64
In [19]:
f,ax=plt.subplots(figsize=(8,6))
sns.countplot(x="Preferred Foot",data=fifa,color='g')
plt.show()
In [20]:
f,ax=plt.subplots(figsize=(8,6))
sns.countplot(x="Preferred Foot",hue="Real Face",data=fifa)
plt.show()
In [21]:
g=sns.catplot(x="Preferred Foot",kind="count",palette="ch:.25",data=fifa)

Explore International Reputation variable¶

In [22]:
fifa['International Reputation'].nunique()
Out[22]:
5
In [23]:
fifa['International Reputation'].unique()
Out[23]:
array([ 5.,  4.,  3.,  2.,  1., nan])
In [24]:
fifa['International Reputation'].value_counts()
Out[24]:
1.0    16532
2.0     1261
3.0      309
4.0       51
5.0        6
Name: International Reputation, dtype: int64
In [25]:
f,ax=plt.subplots(figsize=(8,6))
sns.stripplot(x="International Reputation",y="Potential",data=fifa)
plt.show()

We can add jitter to bring out the distribution of values as follows

In [26]:
f,ax=plt.subplots(figsize=(8,6))
sns.stripplot(x="International Reputation",y="Potential",data=fifa,jitter=0.01)
plt.show()
In [27]:
f,ax=plt.subplots(figsize=(8,6))
sns.stripplot(x="International Reputation",y="Potential",hue="Preferred Foot",data=fifa,jitter=0.2,palette="Set2",dodge=True)
plt.show()
In [28]:
f,ax=plt.subplots(figsize=(8,6))
sns.stripplot(x="International Reputation",y="Potential",hue="Preferred Foot",data=fifa,palette="Set2",size=20,marker="D",edgecolor="gray",alpha=.25)
plt.show()
In [29]:
f,ax=plt.subplots(figsize=(8,6))
sns.boxplot(y=fifa["Potential"])
plt.show()
In [30]:
f,ax=plt.subplots(figsize=(8,6))
sns.boxplot(x="International Reputation",y="Potential",data=fifa)
plt.show()
In [31]:
plt.subplots(figsize=(8,6))
sns.boxplot(x="International Reputation",y="Potential",hue="Preferred Foot",data=fifa,palette="Set3")
plt.show()

Violin Plot¶

In [32]:
f,ax=plt.subplots(figsize=(8,6))
sns.violinplot(x=fifa["Potential"])
plt.show()

We can draw the vertical violinplot grouped by the categorical variable International Reputation as follows

In [33]:
f,ax=plt.subplots(figsize=(8,6))
sns.violinplot(x="International Reputation",y="Potential",data=fifa)
plt.show()
In [34]:
f,ax=plt.subplots(figsize=(8,6))
sns.violinplot(x="International Reputation",y="Potential",hue="Preferred Foot",data=fifa,palette="muted")
plt.show()

We can draw split violins to compare the across the hue variable as follows

In [35]:
f,ax=plt.subplots(figsize=(8,6))
sns.violinplot(x="International Reputation",y="Potential",hue="Preferred Foot",data=fifa,palette="muted",split=True)
plt.show()

Point Plot¶

f,ax=plt.subplots(figsize=(8,6)) sns.pointplot(x="International Reputation",y="Potential",data=fifa) plt.show()

In [36]:
f,ax=plt.subplots(figsize=(8,6))
sns.pointplot(x="International Reputation",y="Potential",hue="Preferred Foot",data=fifa)
plt.show()

We can separate the points for different hue levels along the categorical axis as follows

In [37]:
f,ax=plt.subplots(figsize=(8,6))
sns.pointplot(x="International Reputation",y="Potential",hue="Preferred Foot",data=fifa,dodge=True)
plt.show()

We can use a different marker and line style for the hue levels as follows

In [38]:
f,ax=plt.subplots(figsize=(8,6))
sns.pointplot(x="International Reputation",y="Potential",hue="Preferred Foot",data=fifa,markers=["o","x"],linestyles=["-","--"])
plt.show()

Barplot¶

In [39]:
f,ax=plt.subplots(figsize=(8,6))
sns.barplot(x="International Reputation",y="Potential",data=fifa)
plt.show()
In [40]:
f,ax=plt.subplots(figsize=(8,6))
sns.barplot(x="International Reputation",y="Potential",hue="Preferred Foot",data=fifa)
plt.show()

We can use median as the estimate of central tendency as follows

In [41]:
from numpy import median
f,ax=plt.subplots(figsize=(8,6))
sns.barplot(x="International Reputation",y="Potential",data=fifa,estimator=median)
plt.show()

We can show the standard error of the mean with the error bars as follows

In [42]:
f,ax=plt.subplots(figsize=(8,6))
sns.barplot(x="International Reputation",y="Potential",data=fifa,ci=68)
plt.show()

We can show standard deviation of observations instead of a confidence interval as follows

In [43]:
f,ax=plt.subplots(figsize=(8,6))
sns.barplot(x="International Reputation",y="Potential",data=fifa,ci="sd")
plt.show()

We can add “caps” to the error bars as follows

In [44]:
f,ax=plt.subplots(figsize=(8,6))
sns.barplot(x="International Reputation",y="Potential",data=fifa,capsize=0.3)
plt.show()

seaborn relplot function¶

Seaborn relplot() function helps us to draw figure-level interface for drawing relational plots onto a FacetGrid.

This function provides access to several different axes-level functions that show the relationship between two variables with semantic mappings of subsets.

The kind parameter selects the underlying axes-level function to use-

scatterplot() (with kind="scatter"; the default)

lineplot() (with kind="line")

We can plot a scatterplot with variables Heigh and Weight with Seaborn relplot() function as follows

In [45]:
g=sns.relplot(x="Overall",y="Potential",data=fifa)

Seaborn scatterplot() function

This function draws a scatter plot with possibility of several semantic groups.

The relationship between x and y can be shown for different subsets of the data using the hue, size and style parameters.

These parameters control what visual semantics are used to identify the different subsets.

In [46]:
f,ax=plt.subplots(figsize=(8,6))
sns.scatterplot(x="Height",y="Weight",data=fifa)
plt.show()

Seaborn lineplot() function

THis function draws a line plot with possibility of several semantic groupings.

The relationship between x and y can be shown for different subsets of the data using the hue, size and style parameters.

These parameters control what visual semantics are used to identify the different subsets.

In [47]:
f,ax=plt.subplots(figsize=(8,6))
ax=sns.lineplot(x="Stamina",y="Strength",data=fifa)
plt.show()

Seaborn regplot() function

This function plots data and a linear regression model fit. We can plot a linear regression model between Overall and Potential variable with regplot() function as follows-

In [48]:
f,ax=plt.subplots(figsize=(8,6))
ax=sns.regplot(x="Overall",y="Potential",data=fifa)
plt.show()
In [49]:
f,ax=plt.subplots(figsize=(8,6))
ax=sns.regplot(x="Overall",y="Potential",data=fifa,color="g",marker="*")
plt.show()
In [50]:
f,ax=plt.subplots(figsize=(8,6))
ax=sns.regplot(x="International Reputation",y="Potential",data=fifa)
plt.show()

Seaborn Lmplot Function¶

In [51]:
g=sns.lmplot(x="Overall",y="Potential",data=fifa)
In [52]:
g=sns.lmplot(x="Overall",y="Potential",hue="Preferred Foot",data=fifa)
In [53]:
g=sns.lmplot(x="Overall",y="Potential",hue="Preferred Foot",data=fifa,palette="Set1")
In [54]:
g=sns.lmplot(x="Overall",y="Potential",col="Preferred Foot",data=fifa)

Seaborn FacetGrid() function¶

The FacetGrid class is useful when you want to visualize the distribution of a variable or the relationship between multiple variables separately within subsets of your dataset.

A FacetGrid can be drawn with up to three dimensions - row, col and hue. The first two have obvious correspondence with the resulting array of axes - the hue variable is a third dimension along a depth axis, where different levels are plotted with different colors.

The class is used by initializing a FacetGrid object with a dataframe and the names of the variables that will form the row, column or hue dimensions of the grid.

These variables should be categorical or discrete, and then the data at each level of the variable will be used for a facet along that axis.

In [55]:
g=sns.FacetGrid(fifa,col="Preferred Foot")
In [56]:
g = sns.FacetGrid(fifa, col="Preferred Foot")
g = g.map(plt.hist, "Potential")
In [57]:
g=sns.FacetGrid(fifa,col="Preferred Foot")
g=g.map(plt.hist,"Potential",bins=10,color="r")
In [58]:
g=sns.FacetGrid(fifa,col="Preferred Foot")
g=(g.map(plt.scatter,"Height","Weight",edgecolor="w").add_legend())

The size of the figure is set by providing the height of each facet, along with the aspect ratio

In [59]:
g=sns.FacetGrid(fifa,col="Preferred Foot",height=5,aspect=1)
g=g.map(plt.hist,"Potential")

Seaborn Pairgrid() function¶

This function plots subplot grid for plotting pairwise relationships in a dataset.

This class maps each variable in a dataset onto a column and row in a grid of multiple axes.

Different axes-level plotting functions can be used to draw bivariate plots in the upper and lower triangles, and the the marginal distribution of each variable can be shown on the diagonal.

It can also represent an additional level of conditionalization with the hue parameter, which plots different subets of data in different colors.

This uses color to resolve elements on a third dimension, but only draws subsets on top of each other and will not tailor the hue parameter for the specific visualization the way that axes-level functions that accept hue will.

In [60]:
fifa_new=fifa[['Age','Potential','Strength','Stamina','Preferred Foot']]
In [61]:
g=sns.PairGrid(fifa_new)
g=g.map(plt.scatter)
In [62]:
g=sns.PairGrid(fifa_new)
g=g.map_diag(plt.hist)
g=g.map_offdiag(plt.scatter)
In [63]:
g=sns.PairGrid(fifa_new,hue="Preferred Foot")
g=g.map_diag(plt.hist)
g=g.map_offdiag(plt.scatter)
g=g.add_legend()
In [64]:
g=sns.PairGrid(fifa_new,hue="Preferred Foot")
g=g.map_diag(plt.hist,histtype="step",linewidth=3)
g=g.map_offdiag(plt.scatter)
g=g.add_legend()
In [65]:
g=sns.PairGrid(fifa_new,vars=['Age','Stamina'])
g=g.map(plt.scatter)

Seaborn JointGrid() Function¶

In [66]:
g=sns.JointGrid(x="Overall",y="Potential",data=fifa)
g=g.plot(sns.regplot,sns.distplot)
In [67]:
import matplotlib.pyplot as plt
In [69]:
g=sns.JointGrid(x="Overall",y="Potential",data=fifa)
g=g.plot_joint(plt.scatter,color=".5",edgecolor="white")
g=g.plot_marginals(sns.distplot,kde=False,color=".5")
In [71]:
g=sns.JointGrid(x="Overall",y="Potential",data=fifa,space=0)
g=g.plot_joint(sns.kdeplot,cmap="Blues_d")
g=g.plot_marginals(sns.kdeplot,shade=True)
In [72]:
g=sns.JointGrid(x="Overall",y="Potential",data=fifa,height=5,ratio=2)
g=g.plot_joint(sns.kdeplot,cmap="Reds_d")
g=g.plot_marginals(sns.kdeplot,color="r",shade=True)
In [73]:
f,ax=plt.subplots(figsize=(8,6))
ax=sns.regplot(x="Overall",y="Potential",data=fifa)
In [75]:
sns.lmplot(x="Overall",y="Potential",col="Preferred Foot",data=fifa,col_wrap=2,height=5,aspect=1)
plt.show()

Seaborn figure styles¶

There are five preset seaborn themes: darkgrid, whitegrid, dark, white and ticks.

They are each suited to different applications and personal preferences.

The default theme is darkgrid.

The grid helps the plot serve as a lookup table for quantitative information, and the white-on grey helps to keep the grid from competing with lines that represent data.

The whitegrid theme is similar, but it is better suited to plots with heavy data elements:

In [80]:
def sinplot(flip=1):
    x = np.linspace(0, 14, 100)
    for i in range(1, 7):
        plt.plot(x, np.sin(x + i * .5) * (7 - i) * flip)
In [81]:
sinplot()
In [82]:
sns.set()
sinplot()
In [83]:
sns.set_style("whitegrid")
sinplot()
In [84]:
sns.set_style("dark")
sinplot()
In [85]:
sns.set_style("white")
sinplot()
In [87]:
sns.set_style("ticks")
sinplot()
In [ ]: